arXiv Machine Learning October 2, 2026 By Katherine Keegan, Lars Ruthotto

Manifold-Aware Perturbations for Constrained Generative Modeling

Read the original on arXiv Machine Learning →

The paper introduces a new method for perturbing data distributions in a way that respects equality constraints, allowing generative models to better handle constrained data. By adjusting the distribution while preserving the manifold geometry, the approach ensures support matches the ambient space dimension. Experiments with diffusion models and normalizing flows demonstrate improved data recovery and stable sampling across several tasks.

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